Abstract Manager

Road freight and heavy-duty trucks are responsible for close to 9% of the total GHG emissions in Canada [1]. Reducing these emissions is very challenging as trucking demand increases with the economy [2] and the electrification of most heavy-duty vehicles will take a longer than for the other type of road vehicles as their energy requirements are significantly higher [3]. Nevertheless, it is possible to reduce trucking emissions in a relatively short time horizon by improving efficiency. For instance, improving truck efficiency by increasing their loading factor, in other words, maximizing the time when trucks are fully loaded and minimizing when they run without a cargo. This study analyzes the effect of truck loading factor on a major trucking route in Québec (highway 20) and it estimates potential GHG emission economy for different average load-factor scenarios. Results are based on a Monte Carlo analysis made with a tool called the MapEUR [4] which uses different databases on trucks registrations, operation, road characteristics and weather conditions to estimate the fuel consumption of all the trucks travelling of a specific road segment and a given period. The scenario comparison provides better understanding of the potential benefits of increasing load factors and guide the amount of efforts that should be given from fleet operators and policy makers toward such solutions.

Source:

1. Environment and Climate Change Canada (2022) Canadian Environmental Sustainability Indicators: Greenhouse gas emissions. Consulted on 2023-01-29. Available at:https://www.canada.ca/en/environment-climate-change/services/environmental-indicators/greenhouse-gas-emissions.html .

2. McKinnon A (2018) Decarbonizing Logistics: Distribution goods in a low-carbon world, Kogan Page Limited, London

3. Gilbert, R., & Perl, A. (2010). Transport Revolutions: Moving People and Freight Without Oil (2nd ed.). Routledge. https://doi.org/10.4324/9781315781365

4. Samson, N., Levesque, W., Bégin-Drolet, A. & Lépine, J. (2021) Modelling and Mapping of Heavy-Vehicle Energy Consumption. 16th International Symposium on Heavy Vehicle Transport Technology (Quingdao, China)